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IBM Contemplates Integration of In-House AI Chip in New Cloud Service to Lower Cost
IBM Contemplates Integration of In-House AI Chip in New Cloud Service to Lower Cost
Cloudy with Chips

IBM Contemplates Integration of In-House AI Chip in New Cloud Service to Lower Cost

Chip is being manufactured in collaboration with Samsung Electronics

Published Updated

Key takeaways

  • IBM considers integrating in-house AI chips to reduce costs for its new cloud service, 'watsonx'.
  • Collaboration with Samsung Electronics focuses on manufacturing the Artificial Intelligence Unit chip.
  • IBM aims to address previous high costs associated with the Watson system using power-efficient chips.
  • Thousands of prototype chips are already operational, though no release timeline is confirmed.
  • IBM's chip development is not intended as a direct competitor to Nvidia's leading AI semiconductors.
5 min

International Business Machines (IBM) is considering the use of artificial intelligence chips that it designed in-house to lower the costs of operating a cloud computing service it made widely available this week, an executive said, Reuters reported on Tuesday. 

In an interview with Reuters at a semiconductor conference in San Francisco, Mukesh Khare, general manager of IBM Semiconductors, said the company is contemplating using a chip called the Artificial Intelligence Unit as part of its new "watsonx" cloud service.

One of the challenges that the last Watson system experienced was high costs, which IBM hopes to alleviate this time. Since its own chips are particularly power efficient, Khare believes they could cut cloud service costs, the report added. 

Khare has now confirmed that the chip is being manufactured in collaboration with Samsung Electronics, a longstanding partner in IBM's semiconductor research endeavors. 

There is no defined timeframe for when the chip will be accessible for cloud users to use, but according to Khare, the company already has thousands of prototype chips in operation. Khare made it clear that IBM was not trying to design a direct replacement for semiconductors from Nvidia  whose chips lead the market in training AI systems with vast amounts of data.

Besides IBM, Microsoft, Meta, Google, Apple and Amazon have also been working on developing in-house AI chips. For instance, Google has built a supercomputer to train its models with its TPUs (Tensor Processing Units). Apple has been working on M1 and M2 chips for quite some time now. Amazon, on the other hand, is working on Trainium and Inferentia processor architectures. 

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